inevitability-engine_skill

This skill helps you identify inevitable AI-native venture opportunities by mapping capabilities, markets, and business models across horizons.
  • Python

20

GitHub Stars

5

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill leegonzales/aiskills --skill inevitability-engine

  • CHANGELOG.md3.7 KB
  • EVALUATION.md10.4 KB
  • LICENSE2.7 KB
  • README.md9.8 KB
  • SKILL.md15.7 KB

Overview

This skill is a systematic research protocol for discovering AI-native software businesses driven by the rise of synthetic workers. It maps capability trajectories, mines segment × problem matrices, generates business models, validates demand, and computes inevitability scores across 3–24 month horizons. Use it to prioritize automation-native ventures with measurable time-to-revenue and adoption signals.

How this skill works

The engine runs a six‑phase workflow: capability frontier mapping, opportunity discovery, business model generation, market validation, inevitability scoring, and synthesis/output. It cross-references capability timelines (context window, cost, tool reliability) with task decomposition and synthetic worker primitives to produce ranked, time‑bounded business concepts. Final deliverables include a ranked opportunity matrix, deep dives, and an assumptions appendix.

When to use it

  • Exploring AI business opportunities in a specific industry or function
  • Running market research to prioritize automation targets by ROI and feasibility
  • Generating business models that embed synthetic worker roles and SLAs
  • Validating demand, sizing TAM/SAM, and mapping competitive wedges
  • Scoring and ranking opportunities by inevitability across time horizons

Best practices

  • Start with capability mapping to ground ideas in concrete 3–24 month unlocks
  • Use segment × problem matrices to exhaustively surface high-impact tasks
  • Design ideas around synthetic worker primitives (e.g., Research Synthesizer, Workflow Orchestrator)
  • Quantify economic pressure and automatable % before ideation to focus on >10x leverage
  • Validate buyer intent via social proof and simple TAM calculations before deep builds

Example use cases

  • Map what becomes automatable for legal research and generate 5 AI-native legal assistants
  • Build a prioritized list of mid-market automation ideas with inevitability scores for a VC scouting sprint
  • Validate an internal product idea by estimating automatable workflow %, cost delta, and adoption friction
  • Create a 2‑page executive summary and 25‑item opportunity matrix for corporate strategy review
  • Design synthetic operations team concepts for a product-led AI platform with SLAs and eval frameworks

FAQ

A numeric ranking combining economic pressure, technical feasibility, market readiness, and adoption friction to estimate when an opportunity becomes inevitable.

Which time horizons does it support?

Standard horizons are 3, 6, 12, 18, and 24 months, tied to capability unlocks like context window and cost trends.

What outputs will I get?

Executive summary, ranked opportunity matrix, deep dives for top picks, and a research appendix with queries and assumptions.

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